Regression Analysis of Count DataCambridge University Press, 1998 M09 28 - 411 páginas Students in both the natural and social sciences often seek regression models to explain the frequency of events, such as visits to a doctor, auto accidents or job hiring. This analysis provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors combine theory and practice to make sophisticated methods of analysis accessible to practitioners working with widely different types of data and software. The treatment will be useful to researchers in areas such as applied statistics, econometrics, operations research, actuarial studies, demography, biostatistics, and quantitatively-oriented sociology and political science. The book may be used as a reference work on count models or by students seeking an authoritative overview. The analysis is complemented by template programs available on the Internet through the authors' homepages. |
Dentro del libro
Resultados 1-5 de 85
Página viii
... Sample Corrections Conditional Moment Specification Tests 163 168 5.7 Discriminating among Nonnested Models 182 5.8 Derivations 185 5.9 Bibliographic Notes 187 5.10 Exercises 188 6 Empirical Illustrations 189 6.1 Introduction 189 6.2 ...
... Sample Corrections Conditional Moment Specification Tests 163 168 5.7 Discriminating among Nonnested Models 182 5.8 Derivations 185 5.9 Bibliographic Notes 187 5.10 Exercises 188 6 Empirical Illustrations 189 6.1 Introduction 189 6.2 ...
Página ix
... Samples and Simultaneity 326 11.1 Introduction 326 11.2 Alternative Sampling Frames 326 11.3 Simultaneity 331 11.4 Sample Selection 336 11.5 Bibliographic Notes 343 12 Flexible Methods for Counts 344 12.1 Introduction 344 12.2 Efficient ...
... Samples and Simultaneity 326 11.1 Introduction 326 11.2 Alternative Sampling Frames 326 11.3 Simultaneity 331 11.4 Sample Selection 336 11.5 Bibliographic Notes 343 12 Flexible Methods for Counts 344 12.1 Introduction 344 12.2 Efficient ...
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Contenido
IV | 1 |
V | 3 |
VI | 8 |
VII | 10 |
VIII | 15 |
IX | 17 |
X | 19 |
XI | 20 |
LXI | 226 |
LXII | 234 |
LXIII | 238 |
LXIV | 240 |
LXV | 242 |
LXVI | 244 |
LXVII | 245 |
LXVIII | 246 |
XII | 22 |
XIII | 27 |
XIV | 37 |
XV | 44 |
XVI | 50 |
XVII | 57 |
XIX | 59 |
XX | 61 |
XXI | 70 |
XXII | 77 |
XXIII | 79 |
XXIV | 85 |
XXV | 88 |
XXVI | 93 |
XXVII | 94 |
XXVIII | 95 |
XXIX | 96 |
XXX | 97 |
XXXI | 106 |
XXXII | 112 |
XXXIII | 117 |
XXXIV | 121 |
XXXV | 123 |
XXXVI | 128 |
XXXVII | 134 |
XXXVIII | 135 |
XXXIX | 136 |
XL | 137 |
XLI | 139 |
XLII | 140 |
XLIII | 151 |
XLIV | 158 |
XLV | 163 |
XLVI | 168 |
XLVII | 182 |
XLVIII | 185 |
XLIX | 187 |
L | 188 |
LI | 189 |
LII | 190 |
LIII | 192 |
LIV | 207 |
LV | 216 |
LVI | 218 |
LVII | 219 |
LVIII | 220 |
LIX | 221 |
LX | 222 |
LXIX | 250 |
LXXI | 251 |
LXXII | 252 |
LXXIII | 256 |
LXXIV | 260 |
LXXV | 263 |
LXXVI | 269 |
LXXVII | 272 |
LXXVIII | 273 |
LXXIX | 275 |
LXXX | 276 |
LXXXI | 280 |
LXXXII | 287 |
LXXXIII | 290 |
LXXXIV | 293 |
LXXXV | 294 |
LXXXVI | 299 |
LXXXVII | 300 |
LXXXIX | 301 |
XC | 302 |
XCI | 307 |
XCII | 309 |
XCIII | 313 |
XCIV | 323 |
XCV | 324 |
XCVI | 325 |
XCVII | 326 |
XCVIII | 331 |
XCIX | 336 |
C | 343 |
CI | 344 |
CII | 345 |
CIII | 350 |
CIV | 356 |
CV | 358 |
CVI | 364 |
CVII | 367 |
CIX | 369 |
CX | 371 |
CXI | 374 |
CXII | 375 |
CXIII | 376 |
CXIV | 378 |
379 | |
399 | |
404 | |
Términos y frases comunes
alternative analysis application approach assumption asymptotically bivariate bootstrap Chapter coefficients component conditional mean function consider consistent estimator correctly specified count data models count models covariates defined denote density dependent variable deviance doctor visits Econometrics effects model equation example exponential family finite mixture first-order conditions fixed effects frequency given Gurmu Hausman heteroskedasticity hurdle model individual Journal likelihood function linear model LM test log-likelihood maximum likelihood estimation measurement errors methods misspecification mixture model multivariate NB2 model negative binomial nonlinear normal number of events observations obtained ordered probit overdispersion parameters Poisson distribution Poisson model Poisson PMLE Poisson process Poisson regression Poisson regression model polynomial probability random effects random effects model random variable regressors residuals sample serial correlation standard errors test statistic truncated underdispersion unobserved heterogeneity variance function variance matrix vector y₁ zero
Referencias a este libro
Univariate Discrete Distributions Norman L. Johnson,Adrienne W. Kemp,Samuel Kotz Vista previa limitada - 2005 |
Multivariate Statistical Modelling Based on Generalized Linear Models Ludwig Fahrmeir,Gerhard Tutz Sin vista previa disponible - 2001 |